Highly Accurate and Memory Efficient Unsupervised Learning-Based Discrete CT Registration Using 2.5D Displacement Search

Abstract

Learning-based registration, in particular unsupervised approaches that use a deep network to predict a displacement field that minimise a conventional similarity metric, has gained huge interest within the last two years. It has, however, not yet reached the high accuracy of specialised conventional algorithms for estimating large 3D deformations. Employing a dense set of discrete displacements (in a so-called correlation layer) has shown great success in learning 2D optical flow estimation, cf. FlowNet and PWC-Net, but comes at excessive memory requirements when extended to 3D medical registration. We propose a highly accurate unsupervised learning framework for 3D abdominal CT registration that uses a discrete displacement layer and a contrast-invariant metric (MIND descriptors) that is evaluated in a probabilistic fashion. We realise a substantial reduction in memory and computational demand by iteratively subdividing the 3D search space into orthogonal planes. In our experimental validation on inter-subject deformable 3D registration, we demonstrate substantial improvements in accuracy (at least ≈ 10% points Dice) compared to widely used conventional methods (ANTs SyN, NiftyReg, IRTK) and state-of-the-art U-Net based learning methods (VoxelMorph). We reduce the search space 5-fold, speed-up the run-time twice and are on-par in terms of accuracy with a fully 3D discrete network.